The present invention relates generally to methods for multivariate calibration and prediction and their application to the non-invasive or non-destructive measurement of selected properties utilizing spectroscopy methods. A specific implementation of the invention relates to multivariate calibration and prediction methods utilized in spectroscopic analysis wherein a biological sample or tissue is irradiated with infrared energy having at least several wavelengths, and differential absorption by the biological sample or tissue is measured to determine an analyte concentration or other attribute of the sample or tissue by application of the calibration model to the resulting spectral information.
The need and demand for an accurate, non-invasive method for determining attributes of tissue, other biological samples, or analyte concentrations in tissue or blood are well documented. For example, accurate non-invasive measurement of blood glucose levels in patients, particularly diabetics, would greatly improve treatment. Barnes et al. (U.S. Pat. No. 5,379,764) disclose the necessity for diabetics to frequently monitor glucose levels in their blood. It is further recognized that the more frequent the analysis, the less likely there will be large swings in glucose levels. These large swings are associated with the symptoms and complications of the disease, whose long-term effects can include heart disease, arteriosclerosis, blindness, stroke, hypertension, kidney failure, and premature death. As described below, several systems have been proposed for the non-invasive measurement of glucose in blood. However, despite these efforts, a lancet cut into the finger is still necessary for all presently commercially available forms of home glucose monitoring. This is believed so compromising to the diabetic patient that the most effective use of any form of diabetic management is rarely achieved.
The various proposed non-invasive methods for determining blood glucose level generally utilize quantitative infrared spectroscopy as a theoretical basis for analysis. In general, these methods involve probing glucose containing tissue using infrared radiation in absorption or attenuated total reflectance mode. Infrared spectroscopy measures the electromagnetic radiation (0.7-25 xcexcm) a substance absorbs at various wavelengths. Molecules do not maintain fixed positions with respect to each other, but vibrate back and forth about an average distance. Absorption of light at the appropriate energy causes the molecules to become excited to a higher vibration level. The excitation of the molecules to an excited state occurs only at certain discrete energy levels, which are characteristic for that particular molecule. The most primary vibrational states occur in the mid-infrared frequency region (i.e., 2.5-25 xcexcm). However, non-invasive analyte determination in blood in this region is problematic, if not impossible, due to the absorption of the light by water. The problem is overcome through the use of shorter wavelengths of light which are not as attenuated by water. Overtones of the primary vibrational states exist at shorter wavelengths and enable quantitative determinations at these wavelengths.
It is known that glucose absorbs at multiple frequencies in both the mid- and near-infrared range. There are, however, other infrared active analytes in the tissue and blood that also absorb at similar frequencies. Due to the overlapping nature of these absorption bands, no single or specific frequency can be used for reliable non-invasive glucose measurement. Analysis of spectral data for glucose measurement thus requires evaluation of many spectral intensities over a wide spectral range to achieve the sensitivity, precision, accuracy, and reliability necessary for quantitative determination. In addition to overlapping absorption bands, measurement of glucose is further complicated by the fact that glucose is a minor component by weight in blood and tissue, and that the resulting spectral data may exhibit a non-linear response due to both the properties of the substance being examined and/or inherent non-linearities in optical instrumentation.
A further common element to non-invasive glucose measuring techniques is the necessity for an optical interface between the body portion at the point of measurement and the sensor element of the analytical instrument. Generally, the sensor element must include an input element or means for irradiating the sample point with the infrared energy. The sensor element must further include an output element or means for measuring transmitted or reflected energy at various wavelengths resulting from irradiation through the input element. The optical interface also introduces variability into the non-invasive measurement.
Robinson et al. (U.S. Pat. No. 4,975,581) disclose a method and apparatus for measuring a characteristic of unknown value in a biological sample using infrared spectroscopy in conjunction with a multivariate model that is empirically derived from a set of spectra of biological samples of known characteristic values. The above-mentioned characteristic is generally the concentration of an analyte, such as glucose, but also may be any chemical or physical property of the sample. The method of Robinson et al. involves a two-step process that includes both calibration and prediction steps. In the calibration step, the infrared light is coupled to calibration samples of known characteristic values so that there is differential attenuation of at least several wavelengths of the infrared radiation as a function of the various components and analytes comprising the sample with known characteristic value. The infrared light is coupled to the sample by passing the light through the sample or by reflecting the light from the sample. Absorption of the infrared light by the sample causes intensity variations of the light that are a function of the wavelength of the light. The resulting intensity variations at the at least several wavelengths are measured for the set of calibration samples of known characteristic values. Original or transformed intensity variations are then empirically related to the known characteristic of the calibration samples using a multivariate algorithm to obtain a multivariate calibration model. In the prediction step, the infrared light is coupled to a sample of unknown characteristic value, and the calibration model is applied to the original or transformed intensity variations of the appropriate wavelengths of light measured from this unknown sample. The result of the prediction step is the estimated value of the characteristic of the unknown sample. The disclosure of Robinson et al. is incorporated herein by reference.
Barnes et al. (U.S. Pat. No. 5,379,764) disclose a spectrographic method for analyzing glucose concentration wherein near infrared radiation is projected on a portion of the body, the radiation including a plurality of wavelengths, followed by sensing the resulting radiation emitted from the portion of the body as affected by the absorption of the body. The method disclosed includes pretreating the resulting data to minimize influences of offset and drift to obtain an expression of the magnitude of the sensed radiation as modified.
Dxc3xa4hne et al. (U.S. Pat. No. 4,655,225) disclose the employment of near infrared spectroscopy for non-invasively transmitting optical energy in the near-infrared spectrum through a finger or earlobe of a subject. Also discussed is the use of near infrared energy diffusely reflected from deep within the tissues. Responses are derived at two different wavelengths to quantify glucose in the subject. One of the wavelengths is used to determine background absorption, while the other wavelength is used. to determine glucose absorption.
Caro (U.S. Pat. No. 5,348,003) discloses the use of temporally modulated electromagnetic energy at multiple wavelengths as the irradiating light energy. The derived wavelength dependence of the optical absorption per unit path length is compared with a calibration model to derive concentrations of an analyte in the medium.
Wu et al. (U.S. Pat. No. 5,452,723) disclose a method of spectrographic analysis of a tissue sample which includes measuring the diffuse reflectance spectrum, as well as a second selected spectrum, such as fluorescence, and adjusting the spectrum with the reflectance spectrum. Wu et al. assert that this procedure reduces the sample-to-sample variability.
The intended benefit of using models such as those disclosed above, including multivariate analysis as disclosed by Robinson, is that direct measurements that are important but costly, time consuming, or difficult to obtain, may be replaced by other indirect measurements that are cheaper and easier to get. However, none of the prior art modeling methods, as disclosed, has proven to be sufficiently robust or accurate to be used as a surrogate or replacement for direct measurement of an analyte such as glucose.
Of particular importance to the present invention is the use of multivariate analysis. Measurement by multivariate analysis involves a two-step process. In the first step, calibration, a model is constructed utilizing a data set obtained by concurrently making indirect measurements and direct measurements (e.g., by invasively drawing or taking and analyzing a biological sample such as blood for glucose levels) in a number of situations spanning a variety of physiological and instrumental conditions. A general form for the relationship between direct (blood-glucose concentration) and the indirect (optical) measurements is Ĝ=ƒ(y1, y2, . . . , yq), where Ĝ is the desired estimated value of the direct measurement (glucose), ƒ is some function (model), and y1, y2, . . . , yq (the arguments of ƒ) represents the indirect (optical) measurement, or transformed optical measurements, at q wavelengths. The goal of this first step is to develop a useful function, ƒ. In the second step, prediction, this function is evaluated at a measured set of indirect (optical) measurements {y1, y2, . . . , yq} in order to obtain an estimate of the direct measurement (blood-glucose concentration) at some time in the future when optical measurements will be made without a corresponding direct or invasive measurement.
Ideally, one would prefer to develop a calibration model that is applicable across all subjects and all instruments (i.e., instruments used to make the measurements). The ability to use a calibration developed on one instrument on another instrument is referred to as calibration transfer. The instrument or instruments that are used for collection of the calibration data are referred to as master instruments. Master instruments can be completely different instruments or an instrument(s) that are modified to produce different instrument conditions or states. The master instruments are used to produce calibration data which is typically composed of spectra and direct reference values. The calibration data can be used in raw form or processed in multiple ways to create calibration information. Calibration information can be simply the raw data, a calibration model, an eigenvector decomposition of the data, or any other suitable representation of the information content contained in the master calibration data. The calibration information is then used by a slave instrument such that the slave instrument can make prediction measurements. A slave instrument is simply an instrument that uses the master calibration information. In practice, the slave instrument is a production version of the master instruments. The slave instrument is manufactured to be the same as the master instrument, but variances in manufacturing result in measurable differences. The development of a single calibration model that works across these manufacturing differences is referred to as a universal model. A universal model or calibration is a calibration that can be transferred from the master instrument or instruments to the slave without adaptation, correction or other modifications. Universal models have been referred to as global calibration models in the literature. However, it has been shown that for many applications, subject and instrument variability make it difficult to develop a universal calibration model. Subject and instrument variability are specifically addressed in U.S. patent application Ser. No. 09/415,432, which has been incorporated by reference. The magnitude and general complexity of variation can be characterized by the standard deviation of the spectral data. FIG. 1 graphically illustrates the difference between inter-instrument variation and intra-instrument variation. The spectral data used to generate the figure was acquired over a six-week period and utilized 175 background measurements made on three different instruments. The inter-instrument variation is the standard deviation of the spectral data acquired over the time period. The intra-instrument variation was calculated by first meancentering the spectral data by instrument with subsequent calculation of the standard deviation. The spectral variation across instruments, inter-instrument spectral variation, is substantially larger than the intra-instrument variation and has a more complex spectral shape. The inter-instrument variation includes all spectral differences between the instruments, as well as the intra-instrument variations observed over the data acquisition period. Sources of spectral variation within an instrument include alignment changes, environmental changes, etc. The spectral variation across instruments is substantially larger than the sum of all effects within an instrument. Thus, the task of building a universal calibration model that will be effective across instruments is a daunting one.
Various attempts have been made to address instrument variability, but with limited success. For example, U.S. Pat. No. 4,866,644 to Shenk et al. teaches a method of developing an explicit correction for the spectra generated by each field instrument based upon the measurement of a common set of standard samples measured on the master and field instruments. U.S. Pat. No. 5,243,546 to Maggard teaches a method of developing an explicit correction to the calibration model for each field instrument based upon the measurement of a common set of standard samples measured on the master and field instruments. U.S. Pat. No. 5,459,677 to Kowalski et al. teaches a method of developing an explicit correction (xe2x80x9ctransfer coefficientsxe2x80x9d) for the spectra generated by each field (xe2x80x9ctargetxe2x80x9d) instrument based upon the measurement of a common set of standard samples measured on the master (xe2x80x9creferencexe2x80x9d) and field instruments. U.S. Pat. No. 5,552,997 to Massart teaches a method of developing and validating an explicit univariate calibration for each analytical instrument based upon the measurement of a set of standard samples with known reference values measured on the instrument of interest, allowing for changes in bias, slope and curvature. However, Massart does not address transfer of calibration, nor does Massart address a multivariate framework. U.S. Pat. No. 5,724,268 to Sodickson et al. teaches a method of estimating and compensating for spectral errors introduced by spectroscopic instrumentation by estimating and accounting for the error sources using least-squares or other mathematical estimation techniques.
A number of methods have also been proposed in the literature for transferring a calibration from one near-infrared spectrometer based instrument to another. These methods may be classified into four general categories: (1) pre-processing, (2) hybrid models, (3) wavelength selection, and (4) transformations. Methods within each category may be generally effective at compensating for certain instrument-to-instrument differences.
A pre-processing method is described in C. E. Anderson, J. H. Kalivas, xe2x80x9cFundamentals of Calibration Transfer Through Procrustes Analysisxe2x80x9d, Appl. Spectros., 53(10), 1268 (1999). This method employs a statistical methodology called Procrustes analysis and, in particular, highlights a process they call translation. The authors conclude that xe2x80x9ctranslation is the key step for transformation of spectra and may often be all that is requiredxe2x80x9d to achieve calibration transfer. This technique requires a common set of samples to be measured on both the master and slave instruments.
Another pre-processing method called xe2x80x9corthogonal signal correctionxe2x80x9d is described by J. Sjoblom et al. in xe2x80x9cAn Evaluation of Orthogonal Signal Correction Applied to Calibration Transfer of Near Infrared Spectraxe2x80x9d, Chemom and Intell Lab. Sys., 44, 229 (1998). This method again requires a common set of samples to be measured on both the master and slave instruments and is reported to perform at about the same level as other known calibration transfer methods (piece-wise direct standardization and hybrid modeling).
Another pre-processing method wherein the derivative spectra are used for calibration and validation is compared to piece-wise direct standardization (PDS) in H. Swierenga et al., xe2x80x9cComparison of Two Different Approaches Toward Model Transferability in NMR Spectroscopyxe2x80x9d, Appl. Spectros., 52(1), 7 (1998). It was reported that, in some cases, using derivative spectra was as effective as PDS, but in other cases, it performed poorly compared to PDS.
Hybrid modeling, wherein samples measured on both instruments are used directly in building the calibration, has been applied to a calibration transfer problem as described in D. Ozdemir et al., xe2x80x9cHybrid Calibration Models: An Alternative to Calibration Transferxe2x80x9d, Appl. Spectros., 52(4), 599 (1998). Results reportedly show that when using a multivariate analysis method such as partial least squares (PLS) to build a calibration, effective models may be constructed, but equal number of samples must be measured on both the master and slave instruments.
Wavelength selection, a method which attempts to identify and use only those wavelengths that contain information pertinent to the analyte of interest and minimize the inclusion of wavelengths that contain only instrument-specific data, has been applied to problems in calibration transfer. It has been reported by H. Swierenga et al. in xe2x80x9cImprovement of PLS Model Transferability by Robust Wavelength Selection.xe2x80x9d, Chemom. Intell. Lab. Syst., 14, 237 (1998) that wavelength selection can perform calibration transfers as effectively as PDS.
Direct standardization and piece-wise direct standardization are methods used for calibration transfer that rely on the measurement of a number of standard samples on both the master and slave instruments. These methods are described by Y-D. Wang and B. R. Kowalski in xe2x80x9cCalibration Transfer and Measurement Stability of Near-Infrared Spectrometersxe2x80x9d, Appl. Spectros., 46(5), 764 (1992) and others (see, e.g., Y-D. Wang, M. J. Lysaght, B. R. Kowalski, xe2x80x9cImprovement of Multivariate Calibration Through Instrument Standardizationxe2x80x9d, Anal. Chem., 64, 562 (1992); and Z. Wang, xe2x80x9cAdditive Background Correction in Multivariate Instrument Standardizationxe2x80x9d, Anal. Chem., 67, 2379 (1995)).
A technique called xe2x80x9coptical matchingxe2x80x9d is reported by B. G. Osborne et al. in xe2x80x9cOptical Matching of Near Infrared Reflectance Monochromator Instruments for the Analysis of Ground and Whole Wheatxe2x80x9d, J. Near Infrared Spectrosc., 7, 167 (1999). This method again requires the use of a set of transfer samples measured on both instruments.
Techniques employing finite impulse response filters have been described by S. T. Sum and S. D. Brown in xe2x80x9cStandardization of Fiber Optic Probes for Near-Infrared Multivariate Calibrationsxe2x80x9d, Appl. Spectros., 52(6), 869 (1998) and by T. B. Blanket al. in xe2x80x9cTransfer of Near-Infrared Multivariate Calibrations Without Standardsxe2x80x9d, Anal. Chem., 68, 2987 (1996). Although FIR filtering methods were generally found to be successful, this method was not as effective as PDS when a bias was present between the master and slave instruments.
In addition, it should be noted that efforts have been made to create calibration models that are robust to various instrumental changes that may occur after the calibration period. In xe2x80x9cStrategy for Constructing Robust Multivariate Calibration Modelsxe2x80x9d, Chemometrics and Intelligent Laboratory Systems, 49, 1-17 (1999), Swierenga et al. describe methods of assessing a calibration""s sensitivity to environmental effects and apply various pre-processing techniques on the calibration set in order to reduce this sensitivity.
A method of selecting xe2x80x9crobust variablesxe2x80x9d, resulting in a more robust calibration, is described by Swierenga et al. in xe2x80x9cDevelopment of Robust Calibration Models in Near Infra-Red Spectrometric Applicationsxe2x80x9d, Anal. Chim. Acta, 411, 121-135 (2000). This work compares the effectiveness of selecting xe2x80x9crobust variablesxe2x80x9d with the method of including the external variations in the calibration set.
Ozdemir et al. report in xe2x80x9cEffect of Wavelength Drift on Single and Multi-Instrument Calibration Using Genetic Regressionxe2x80x9d, Applied Spectroscopy, 52, 1203-1209 (1998) that, in simulation, inclusion of wavelength shifted spectra in the calibration serves to make the model more robust to wavelength shifts in the spectra of the validation set.
Near-infrared spectroscopy has been applied to many quantitative and qualitative analysis problems encountered in both academia and industry. Various techniques for creating a useful calibration model for a particular instrument have been proposed as discussed previously (for example, see Multivariate Calibration, H. Martens and T. Naes, 1989, Wiley and Sons Ltd.), but effective techniques for maintaining this calibration model on the same instrument across changes to the environment or instrument, or transferring the calibration model to another instrument have not been universally accepted.
The need for applying a single calibration model to multiple instruments arises in a variety of fields including, but not limited to, process and quality control. Applying a calibration model from one instrument to data collected on another (slave) instrument is made difficult by differences in instruments that give rise to a number of spectral effects (for example, instrument response, resolution, photometric accuracy, etc.). These differences will tend to introduce elevated errors in the predictions made by the slave instrument. These additional prediction errors can, in general, be classified as due to some combination of bias, slope, and precision. Bias errors are those that represent a fixed error, common to all predictions made on the slave instrument. Slope errors are those that are proportional to the magnitude of the attribute of biological tissue being measured, such as glucose concentration. Precision errors are calculated as the additional prediction error that is not ascribable to bias or slope.
In general, the process of creating a calibration model for a particular instrument is time consuming and expensive, and therefore impractical for applications requiring the use of multiple instruments or using a single instrument in different environments or with different sampling accessories. A method for transferring a calibration from one (master) instrument to another (slave) instrument (or multiple slave instruments) with minimal effort would be beneficial in a wide variety of fields employing near infrared spectroscopy.
Accordingly, the need exists for a method and apparatus for non-invasively measuring attributes of biological tissue, such as glucose concentrations in blood, which incorporates a model that is sufficiently robust to act as an accurate surrogate for direct measurement. The model would preferably account for instrument and subject variability. Specifically, the methods and apparatus should provide a model that eliminates or significantly reduces all forms of excess prediction error manifested as bias, slope or precision errors. In order to be commercially successful, applicants believe, the model should not require extensive sampling of the specific instrument and/or subject on which the model is to be applied in order to accurately predict a biological attribute such as glucose.
The present invention addresses these needs as well as other problems associated with existing models and calibrations used in methods for non-invasively measuring an attribute of a biological sample such as glucose concentration in blood. The present invention also offers further advantages over the prior art and solves problems associated therewith.
The present invention is a method that reduces the level of interfering spectral variation that a multivariate calibration model needs to compensate for. An important application of the invention is the non-invasive measurement of an attribute of a biological sample such as an analyte, particularly glucose, in human tissue. The invention utilizes spectroscopic techniques in conjunction with improved protocols and methods for acquiring and processing spectral data. The essence of the invention consists of protocols and data-analytic methods that enable a clear definition of intra-instrument spectral effects while reducing inter-instrument spectral effects. The resulting data, which have reduced inter-instrument spectroscopic variation, can be utilized in a prediction method that is specific for a given instrument or tailored (or adapted) for use on the specific instrument. The prediction method uses a minimal set of reference samples from that instrument for generation of valid prediction results.
A preferred method for non-invasively measuring a tissue attribute, such as the concentration of glucose in blood, includes first providing an apparatus for measuring infrared absorption by a biological sample such as an analyte containing tissue. The apparatus preferably includes generally three elements, an energy source, a sensor element, and a spectrum analyzer. The sensor element includes an input element and an output element. The input element is operatively connected to the energy source by a first means for transmitting infrared energy. The output element is operatively connected to the spectrum analyzer by a second means for transmitting infrared energy.
In practicing a preferred method of the present invention, an analyte containing tissue area is selected as the point of analysis. This area can include the skin surface on the finger, earlobe, forearm, or any other skin surface. A preferred sample location is the underside of the forearm. The sensor element, which includes the input element and the output element, is then placed in contact with the skin. In this way, the input element and output element are optically coupled to the analyte containing tissue or skin surface
In analyzing for a biological attribute, such as the concentration of glucose in the analyte containing tissue, light energy from the energy source is transmitted via a first means for transmitting infrared energy into the input element. The light energy is transmitted from the input element to the skin surface. Some of the light energy contacting the analyte-containing sample is differentially absorbed and scattered by the various components and analytes contained therein at various depths within the sample. A quantity of light energy is reflected back to the output element. The non-absorbed reflected light energy is then transmitted via the second means for transmitting infrared energy to the spectrum analyzer. As detailed below, the spectrum analyzer preferably utilizes a computer and associated memory device to generate a prediction result utilizing the measured intensities and a calibration model from which a multivariate algorithm is derived.
The viability of the present invention to act as an accurate and robust surrogate for direct measurement of biological attributes in a sample such as glucose in tissue, resides in the ability to generate accurate predictions of the direct measurement (e.g., glucose level) via the indirect measurements (spectra). Applicants have found that, in the case of the noninvasive prediction of glucose by spectroscopic means, application of known multivariate techniques to spectral data will not produce a predictive model that yields sufficiently accurate predictions for future use. In order to obtain useful predictions, the spectral contribution from the particular analyte or attribute of interest must be extracted from a complex and varying background of interfering signals. The interfering signals vary across and within instruments and can be broadly partitioned into xe2x80x9cintra-instrumentxe2x80x9d and xe2x80x9cinter-instrumentxe2x80x9d sources. Some of these interfering signals arise from fabrication differences between instruments. The net effect of the cumulative interfering signals due to inter-instrument variations is a degradation in performance when the calibration developed in one instrument (hereinafter referred to as the master instrument) is used to generate prediction results on another instrument (hereinafter referred to as the slave instrument). This degradation in performance can be reduced or minimized by building identical or clone instruments, but this strict requirement of sameness can increase production cost, if such level of sameness is even possible.
The present invention involves a prediction process that reduces the impact of instrument-specific effects on prediction through a tailoring process, while concurrently facilitating the modeling of intra-instrument effects. The tailoring process is used to adapt the model so that it predicts accurately for a given instrument. An essential experimental observation is that intra-instrument spectral effects are moderately consistent across instruments that are built in a similar manner. Thus, intra-instrument spectral variation observed from a set of instruments can be used to enhance or strengthen the calibration for subsequent use on an individual instrument not included in the set. This results in a prediction process that is specific for use on a given instrument, but where intra-instrument information from other instruments is used to enhance the performance of the monitoring device.
Spectroscopic data that have been acquired and processed in a manner that reduces inter-instrument spectroscopic variation while maintaining intra-instrument variation are herein referred to as generic calibration data. These generic data, which comprise a library of intra-instrument variation, are representative of the likely variation that might be observed over time for any particular instrument. In order to be effective, the intra-instrument spectral variation manifested in the generic calibration data must be representative of future intra-instrument spectral effects.
Intra-instrument effects can be caused by many influences, some of which are listed below. Changes in the illumination system due to alignment changes, environmental changes such as humidity or temperature, bulb changes, optical filter aging, optical filter changes due to temperature, changes in optical surface quality, bulb aging, and power supply fluctuations. Changes in the detector or detectors due to temperature changes of the detector, linearity changes, environmental changes, or response changes. Changes in the data acquisition system due to temperature changes of the electronics, linearity changes, environmental changes, or response changes. Changes in the instrument-sample interface to include changes in throughput, pathlength, sampling error, changes in optical surface quality or spectral response. Spectrometer changes due to environmental changes, chromatic aberration, spatial response function, angular response function, component changes due to aging, vibration, changes in optical surface quality, temperature and humidity, changes in modulation efficiency, throughput, wavelength drift, and apodization changes. The preceding list of intra-instrument sources of variation is an incomplete list of all the different types of variation that are present in optical instrumentation. It is important to note that the generic calibration data preferably include spectroscopic effects associated with the instrument utilized. Thus, it is important to use an appropriate experimental protocol to provide representation of these intra-instrument spectral effects.
In each prediction embodiment of the present invention, multivariate techniques are applied to the generic calibration data to derive an instrument-specific predictor of the direct measurement. Each prediction embodiment uses the generic calibration data in some raw or altered condition in conjunction with at most a few reference spectra from a specific instrument to achieve a tailored prediction method that is an accurate predictor of a desired indirect measurement for that particular instrument. Reference spectra are spectroscopic measurements from a specific instrument that are used in the development of a tailored prediction model. Reference analyte values quantify the concentration of the analyte (via direct methods) and can be used in the development of a tailored prediction model. Applicants have developed several embodiments that incorporate the above concepts.
Each tailored prediction method described herein utilizes generic calibration data. Generic calibration data can be created by a variety of data acquisition and processing methods. In a first preferred processing method, the generic calibration data are obtained by acquiring a series of indirect measurements from one or more instruments and a direct measurement for each instrument corresponding to each indirect measurement. It is important to note that intra-instrument variation can be captured many different ways. In one case, a single instrument can be observed in many different instrument states by subjecting the instrument to conditions that result in intra-instrument variation. For example changing the bulb, changing bulb power, temperature cycling the instrument, etc., can create different intra-instrument states. In an alternative case, multiple instruments can be used to capture different intra-instrument states. An appropriate experimental protocol is needed to provide adequate representation of intra-instrument effects that are expected in the future (including those associated with the instrument of interest). The mean indirect measurement and the mean direct measurement for each instrument based on the number of measurements from that instrument are then formed. The indirect measurements are meancentered by subtracting the mean indirect measurement of each instrument from each of that instrument""s indirect measurements. The direct measurements are meancentered by subtracting the mean direct measurement of each instrument from each of that instrument""s direct measurements. That is, the instrument-specific mean indirect measurements and instrument-specific mean direct measurements act as instrument-specific subtrahends. The sets of meancentered measurements (indirect and direct) comprise the generic calibration data.
There are a number of other related ways for creating generic calibration data with an instrument-specific subtrahend. For example, the instrument-specific subtrahends for the indirect and direct measurements could be some linear combination of each instrument""s indirect and direct measurements, respectively.
In one other specific method for creating generic calibration data, the instrument-specific subtrahends for the indirect and direct measurements consist of the mean of the first S indirect measurements of each instrument and the mean of the first S direct measurements of each instrument, respectively. Alternately, a moving window reference technique could be utilized wherein the subtrahends are the instrument-specific means of the S nearest (in time) indirect and direct measurements, where S is less than the total number of reference measurements made on a particular instrument. The value of S can be chosen to fit the constraints of the particular application, neglecting effects due to random noise and reference error.
In another alternative processing method, the generic calibration data can be produced in a round-robin reference manner wherein one subtracts each instrument""s reference data from every other instrument""s reference measurement made on that instrument in a round-robin fashion.
In a further alternative processing method which is particularly useful when a spectral library associated with a large number of instruments exists, the generic calibration data is created by subtracting some linear combination of spectral library data in order to minimize inter-instrument spectral features. Instrument-specific attributes can be reduced by subtracting some linear combination of similar spectra. That is, the instrument-specific subtrahend for a given instrument consists of a linear combination of spectra obtained from one or more instruments, each of which are different than the given instrument. In one embodiment, the spectrum of a given instrument would be matched with a combination of similarly appearing spectra from other instruments. In another embodiment, one would match the spectrum of a given instrument with a combination of spectra from other instruments where the matching criteria involve measurable parameters such as throughput, modulation efficiency, wavelength axis, absorbance response, chromatic aberration, spectral response, spatial response functions, angular response functions, etc.
In a final alternative processing method, the generic calibration data are created through simulation in a manner that minimizes instrument-specific spectral attributes. This methodology requires accurate modeling and subsequent simulation of the sample under examination, the optical system, the sampler-tissue interface, and all other contributors to spectral variation. Generic calibration data can be simulated directly or instrument data can be simulated. The simulated instrument spectra can subsequently be processed by any of the preceding five processing methods. In an additional embodiment, the simulated data can be combined with real instrument data for the creation of a combined simulated/real generic calibration data.
Once the generic calibration data have been created, such data is then utilized to create a tailored prediction process specific for a particular instrument for use in future predictions of the attribute. The tailored prediction process can be accomplished in several ways.
The most straightforward and direct way to tailor the prediction process to a given instrument is as follows and will be denoted as direct tailoring. First, the generic calibration data are used to develop an intra-instrument calibration model for the instrument of interest. This model herein is referred to as a generic model. By design, the generic model will produce predictions that are essentially unaffected by intra-instrument spectral variation that is represented in the generic calibration data and not associated with the instrument of interest. On the other hand, the generic model will produce predictions that are appropriately sensitive to the instrument of interest. The generic model is applied directly to at least one indirect measurement from a target instrument for which there are corresponding direct measurements. The resulting predictions of the generic model are averaged. The difference between the average of the direct measurements and average prediction is computed. This instrument-specific difference is added to the subsequent predictions of the generic model as applied directly to the future indirect measurements from the target instrument. The resultant sums comprise the net predictions of the direct measurement corresponding to the future indirect measurements from the target instrument. It is important to note that a single generic model can be used in the tailoring process for a number of target instruments.
A second tailored prediction embodiment uses a combination of at least two instrument reference spectra, reference analyte values and the generic calibration data to create a prediction model that is specific for use on the particular instrument. The technique by which the calibration data and reference spectra are combined uses a linear combination of the data in absorbance units. The combinations of calibration data and reference data can be done in a structured or random way. It is the applicants"" observation that random associations work effectively and are easily implemented. The process of creating these composite data is referred to as robustification. The resulting calibration spectra contain the reference spectra from the particular instrument combined with spectral data that contain sources of spectroscopic variation associated with instrument variation such as changes due to illumination system, detector, data acquisition system, sampler, or spectrometer, variations associated with sampling techniques, and spectroscopic effects associated with the instrument of interest. The composite calibration data can be processed to develop a calibration model. The resulting model will be referred to hereafter as a composite calibration model. The resulting composite calibration model is specific for a particular instrument and can be used to generate analyte prediction results for the particular instrument. In the use of either tailored prediction process, reference spectra and reference analyte values are utilized. The reference information is used in combination with the generic calibration data to create a tailored prediction process for use on the particular instrument. In general terms, the instrument reference information is used to tailor a general processing method for use on a particular instrument. In an additional embodiment, the instrument reference spectra can be replaced by the use of an instrument-matched spectrum or a set of matched spectra. Matched spectra are spectra from another instrument or a combined spectrum that interacts with the calibration model in a manner similar to the instrument to be predicted upon. In use, a never-before-seen instrument is tested and at least one spectrum is obtained. The resulting spectrum is used for generating a prediction result and as a reference spectrum. In use and in contrast to the two prior embodiments, no reference analyte value is used or needed. The implementation of this method requires the following:
1. Identification or creation of a matched spectra through use of the reference spectra.
2. Replacement of the reference spectra with the corresponding matched spectra.
3. Although reference analyte values are not obtained from the never-before-seen instrument, matched analyte values from the corresponding matched spectra are used in the processing method in a manner consistent with the prior uses of reference analyte values.
4. Use of either tailored prediction process.
In practice, the spectral data from the never-before-seen instrument is compared with spectral data that has corresponding attribute reference values in a spectral library to identify the best or several matched spectra. Matched spectra are spectra from another instrument that appear similar when processed by the calibration model.
As stated previously, the application of known multivariate analysis techniques for calibration transfer have deficiencies due to cost, complexity, or resulting prediction performance degradation. The processing method described overcomes these known limitations by using a matched spectrum. Thus, the instrument tailoring with this method is accomplished without an actual reference analyte value from the individual instrument. The matched spectrum method in conjunction with either tailored prediction process requires a large spectral library to facilitate the appropriate matching between the instrument to be predicted upon and at least one library spectrum. In implementation of this matching method, applicants have identified matched spectra by finding those spectra that are most consistent with the calibration model as reflected by such parameters as Mahalanobis distance and spectral residual metrics. Other methods of spectral match would also have applicability for determination of matched spectra.
These and various other advantages and features of novelty that characterize the present invention are pointed out with particularity in the claims annexed hereto and forming a part hereof. However, for a better understanding of the invention, its advantages, and the object obtained by its use, reference should be made to the drawings which form a further part hereof, and to the accompanying descriptive matter in which there are illustrated and described preferred embodiments of the present invention.